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Automated Generation of Electronic Warfare Libraries


OBJECTIVE: The objective is to develop technologies that automate the creation of Electronic Warfare (EW) emitter libraries and that will correctly identify and classify threat candidates. DESCRIPTION: The process of generating Electronic Warfare (EW) libraries for current EW systems is a labor intensive and time consuming task because of the metrological differences between the various Electronic Intelligence (ELINT) collection systems and the tactical EW systems. The key parameters measured by ELINT systems are frequency, pulse width; scan type, pulse repetition interval (PRI), range of frequency, PRI stagger type, and others. The parameters are measured with great precision and numerous measurements, in order to gain statistical fidelity when compared with tactical EW systems. Tactical EW systems make classification decisions with a comparatively sparse set of parameters and number of measurements. Consequently they have less accuracy and lower statistical fidelity. This requires the tactical EW libraries that are derived from the national ELINT library databases, to be uniquely tailored or"colorized"to optimize the correct classification of the detected threat. A tool set is needed that automatically resolves the metrological differences of national ELINT systems compared with tactical EW systems. Currently there are no viable options to EW libraries. The tool set would reduce manning costs and prevent the recurring costs associated with engineering a new solution each time. Current"coloring"processes require human interfaces to manually generate emitter scenarios, insert the scenarios into the EW system, note the outputs, and then modify or"color"the library parameters so as to produce a system output that corresponds to the input emitter. This"coloring"process is time consuming, and does not always provide the mathematically optimal and lowest ambiguity emitter correlation. An automated methodology will perform the process more efficiently and optimize the solution in a multi-dimensional space and produce the correct identification with the lowest ambiguity in the required tactical response time (Ref. 1, 2, 3). The Navy is seeking innovative analysis tools that are more encompassing than currently available tools as the amount of specialized data becomes larger and much more complicated in its relationships. The Navy has an urgent need to automate the generation of EW threat libraries to reduce the current labor intensive costly manual process which is inefficient and leads to inaccuracy in threat classification results. Automation will reduce costs and improve EW performance, greatly improving ship and battle group self-defense. The development of this EW tool set will provide the Navy a way to efficiently map existing sensor data into the tactical EW systems, preserve data from loss, and automate the processes that are used to update the EW threat libraries. High Level Program Requirements: 1) The software product shall read the raw data files and create a EW Library File that can be installed and used in an operational environment. 2) The software product shall run on standard PC computers. 3) The software product shall modify raw data to match emitter data. 4) The software product shall have a GUI interface. 5) The company shall deliver a user manual electronically. The Phase I effort will not require access to classified information. If need be, data of the same level of complexity as secured data will be provided to support Phase I work. The Phase II effort will likely require secure access, and the contractor will need to be prepared for personnel and facility certification for secure access. PHASE I: The company will develop innovative software algorithms and computing methods for automating EW library generation described above. The company will demonstrate the feasibility of the algorithms and methods to meet Navy needs and will show the concepts can be feasibly developed into a useful product for the Navy. Feasibility will be shown through testing and analytical scenarios implemented in a simulated environment. The small business will provide a Phase II development plan with performance goals and key technical milestones, and will address technical risk reduction. PHASE II: Based on the results of Phase I and the Phase II development plan, the small business will develop a prototype process for evaluation as appropriate. The prototype will be evaluated to determine its capability in meeting the performance goals defined in the Phase II development plan and the Navy requirements for the new automated EW library generation. System performance will be demonstrated through prototype evaluation and modeling or analytical methods over the required range of parameters including numerous deployment cycles. Evaluation results will be used to refine the prototype into an initial design that will meet Navy requirements of a more efficient and reliable library development tool. The company will prepare a Phase III development plan to transition the technology to Navy use. The company will be provided with suitable test data and have access to Navy facilities as required. PHASE III: If Phase II is successful, the company will be expected to support the Navy in transitioning the automated EW library tool set for Navy use. The company will evaluate the refined prototype tool set to determine its effectiveness in an operationally relevant environment. The company will support the Navy for test and validation to certify and qualify the tool set for Navy use. PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: Radar and combat systems use similar library structures and require similar tools to tailor the tactical systems from national databases that have similar metrological differences. There is potential to use this technology in marine, automotive, and space systems commercially that require the comparison of low quality measured data to higher quality standardized data. REFERENCES: 1) Ludascher, Bertram., Kin, Kai., Bowers, Shawn., Jaeger-Frank, Efrat.,Brodaric, Boyan., Baru, Chaitan."Managing Scientific Data: From Data Integration to Scientific Workflows."1 May 2012.. 2) Zhou, Yinle., Kooshesh, Ali., Talburt, John R."Optimizing the Accuracy of Entity-Based Data Integration of Multiple Data Sources Using Genetic Programming Techniques."International Journal of Business Intelligence Research 3(1) January-March 2012: 72-82 3) Koza, John R."Genetic Programming: A Paradigm For Genetically Breeding Populations Of Computer Programs To Solve Problems."Stanford University. June 1990. Stanford University. 1 May 2012.
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